title: Curation-free biomodules mechanisms in prostate cancer predict recurrent disease.
abstract: BACKGROUND
  Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease.
  
  
  RESULTS
  Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated "Cancer Module" mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank_p = 0.019).
  
  
  CONCLUSIONS
  Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.
authors.affiliation: Center for Biomed Informatics and Department of Medicine, The University of Chicago, Chicago, IL, USA.
authors.forename: James L
authors.initials: JL
authors.lastname: Chen
authors.forename: Alexander
authors.initials: A
authors.lastname: Hsu
authors.forename: Xinan
authors.initials: X
authors.lastname: Yang
authors.forename: Jianrong
authors.initials: J
authors.lastname: Li
authors.forename: Younghee
authors.initials: Y
authors.lastname: Lee
authors.forename: Gurunadh
authors.initials: G
authors.lastname: Parinandi
authors.forename: Haiquan
authors.initials: H
authors.lastname: Li
authors.forename: Yves A
authors.initials: YA
authors.lastname: Lussier
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008297:Male; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000235:genetics; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000145:classification / Q000235:genetics*; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor